Ch. 3

Question 1

Representativeness Heuristic

Answer 1

evaluating the probability of an uncertain event by the degree to which it is

1. Similar in essential properties to its parent population
2. Reflects the salient features of the process by which it is generated……

the ordering of events by their subjective probabilities coincides wit their ordering by representativeness (“does this seem like that?)

Question 2

Representativeness

Answer 2

an assessment of the degree of correspondence between a sample and a population…….. an instance and a category, an act and an actor or, more generally, between an outcome and a model (e.g. judging by resemblance to a stereotype)………… e.g. “does this seem like that?”

Question 3

Judging category membership

In what 2 ways is representativeness applied?

2 acts of substitution

Tom W

Answer 3

representativeness applied in 2 ways – 1. A prototype (representative exemplar) 2. Probability that the individual belongs to a category is judged by the degree of representativeness… …………..

categorical prediction by representativeness involves 2 acts of substitution – substitution of a prototypical exemplar for a category, and the substitution of the heuristic attribute of similarity for the target attribute of probability……………..

Following a person description (Tom W), the judged likelihood that he was in a particular specialisation correlated almost perfectly with ratings of how similar Tom W was to a typical student in each specialisation; participants appeared to neglect the base rates of students in those specialisations.

Question 4

Category membership 2 criteria?

Answer 4

prototypes - (representative exemplars) appear to be used to represent categories…… similarity of the judged thing becomes probability

Question 5

Base rates

Answer 5

the prevalences of particular categories in the environment

Question 6

Prototype heuristic

Answer 6

the use of a prototype to represent a category… … also appear to be involved in other types of judgement, like economic evaluation (e.g. in the bird oil spill)…. Rely on a snapshot model

Question 7

Peak-end effect (snapshot model) heuristic

What is it?

Why is it an instance of representativeness heuristic?

Dirty birds

colonoscopy

Answer 7

judge our past experiences almost entirely on how they were at their peak (pleasant or unpleasant) and how they ended. Other information is not lost, but it is not used. …………..

instance of representativeness heuristic because perceive not the sum of an experience but its average, e.g. only the most salient aspects are recalled

Question 8

Sampling

Answer 8

people made large generalizations refardless of sample size, when they believed that the objects in question were homogenous in the population…….. also though people are aware that statistical reasoning makes sense, they often fail to apply it properly and instead apply the representativeness heuristic (e.g. hospital babies)

Question 9

Law of large numbers

Answer 9

the mean value of a sample is more likely to fall within a specified bound of the parent population the larger that sample is…. …..

people show some intuitive understanding of this; willingness to generalize from a sample depends on beliefs bout variation in the sample/size of sample

Question 10

Compound events

Answer 10

Linda the bank teller, see conjunction fallacy…….. combo of 2 events can’t be more probable than the prob of either event taken individually

Question 11

Conjunction fallacy

What is it?

Linda the bankteller

how can the error be reduced?

Answer 11

estimating the conjunction to be more probable than either individual event; e.g. The Linda problem………

Linda bankteller – people are basing on representativeness rather than actual stats……….

Can reduce this error by providing cues to extensionality (all the events that are contributing to the probability), e.g. parcing them apart in steps

Question 12

Gambler’s fallacy

Answer 12

people treat sequential outcomes of gambling as non-independent, e.g. that past outcomes affect the probs of future outcomes, though all outcomes are still equally likely

Question 13

Hot-hand fallacy

Answer 13

false belief that player who just scored is more likely to score again

Question 14

Availability heuristic

What is it?

Is it generally ecologically valid?

Answer 14

1. estimates frequency or probability by the ease with which instances or associations could be brought to mind
2. ecologically valid clue for judging frequency because more frequent events are easier to recall than infrequent ones
3. can lead to biases, e.g. that recent or more familiar events will have bigger impact on judgment……. E.g. ppl overestimating their own contribution cause its easier to recall
4. heuristic is generally ecologically valid!

Question 15

Recall of content versus ease of retrieval

Answer 15

early studies didn’t distinguish between ease and number/frequency…… ease of recall shown to be main influence (Schwarz, ppl rating themselves more assertive after recalling less # of assertive examples)………

Question 16

Support Theory

What does it distinguish between? What does it do?

How does implicitness/explicitness impact the judging of probability?

Answer 16

Distinguishes between events and hypothesis (decriptions of events)……. people judge the probability of hypotheses rather than events, considering support for hypotheses

Can be implicit (generalized category) or explicit (specific things or cases)…… but people often fail to unpack implicit hypotheses into explicit ones

Thus, support for implicits tends to be subadditive (the spport would be less if it were unpacked into components)

Question 17

subadditivity

Answer 17

tendency to judge prob of the whole to be less than probs of the parts

prob of whole < parts

support for an implicit thing is less than it would be if the hypothesis was unpacked into components

People judging guilties neglected the support for the guilt of other suspects each time they judged an individual, rather it was related to the support

when more evidence introduced the probabilities for each suscpet increased further

Question 18

Minerva DM

Multiple Trace Model?

What’s the process when making a judgment of likelihood under MDM?

How does it support availability heuristic?

Answer 18

integrates theories of judgment process with memory process

multiple-trace memory model - assumes that a new memory trace is created for each new event that is experienced (as ooposed to updating a memory trace)

memory probes are created to access memory, whereby “echoes” strong or weak are produced based on strength of similarity

When making a judgment of likelihood, MDM calculates an “echo intensity” that is based on the sum of the activation of all memory traces

has been used to explain availability phenomena

Question 19

enhancement effect

Answer 19

enhancing subadditivity - increasing support for

Question 20

superadditivity

Question 21

Multiple-trace memory model

Answer 21

assumes a new trace is created for each event experienced (as opposed to updating a trace)

Question 22

memory probe

Answer 22

activation of memory traces that produces an “echo” whose intensity is determined by the similarity to the prompt……..

Question 23

MDM exaplining conjunction error, availability bias?

Answer 23

MM has been used to explain conjunction error - e.g. Linda - feminist vignette contains more details in the description, so that a memory probe for bank teller is weaker compared to feminist………… conjunctive probe for 2 events will return strong echoes from any memoery trace that has either A or B in it, thus exxagerating the echo

and availability - possibly strength of encoding ….. or inability to discriminate between events learned in different contexts, such that it is more salient in general

Question 24

conditional probability

Answer 24

the probability of an event given that some other event has occurred

Bayes’ theorem can help us update beliefs in light of new evidence

Question 25

Baye’s theorem

what do the different P( )s stand for?

Answer 25

P(H | D) =

P(D| H)×P(H)

P(D| H)×P(H)+P(D|~ H)×P(~ H)

P(H) = prior probability, probs that focal hypothesis is true prior to more info being obtained

P(D|H) = if hypothesis true, chacne of observing a certain outcome D

P(D|~H) = observing outcome D if the hypothesis is false

P(H|D) = posterior probability, probs that focal hypoythesis is true given the outcome D has been observed…… What we want to know!

Question 26

conservatism

Answer 26

people insufficiently responsive to new evidence…. dont adjust enough……..

“book bag and poker chip” paradigm (blue or red chips)

however, variations in experimental paradigm seemed to change conservatism degree….. thus it may be just an artefact of experimental conditions

Question 27

base rate neglect

and examples?

Answer 27

base rates don’t have the impact on judgements they should

e.g. Eddy/breast cancer, Gigerenzer HIV tests

Question 28

Eddy/breast cancer conditional prob

Answer 28

Eddy - found that doctors over-relied on mammogram results when reasoning about a hypothertical breat caancer screening…. prior probabilities were neglected

Prob of mammogram detecting it: 79.3

Prob of cancer: 1

Prob of her having cancer after testing positive? 7.7%

Question 29

how do you facilitate bayesian reasoning?

Answer 29

**Rewording problems in the form of frequencies, rather than single-event probabilities, can faciliatate bayesian reasoning(Gigerenzer/Hof)……. **

evolutionary reason for frequencies being dominant is that we encounter events in sequences, e.g. steps

1. 10 out of 1000 women have b cancer
2. 8out of every 10 women with b will get a positive mammogram

Question 30

whats P(H) ?

Answer 30

P(H) = prior probability, probs that focal hypothesis is true prior to more info being obtained

Question 31

What’s P(D|H) ?

Answer 31

P(D|H) = if hypothesis true, chacne of observing a certain outcome D

Question 32

what’s P(D|~H) ?

Answer 32

P(D|~H) = observing outcome D if the hypothesis is false

Question 33

what’s P(H|D)?

posterior probability

Answer 33

P(H|D) = posterior probability, probs that focal hypoythesis is true given the outcome D has been observed…… What we want to know!

Question 34

bayesian reasoning phrased through frequencies - pro and con data?

Answer 34

Pro - correct answers ranged from 46% to 92% when using frequency, and frequency and visual aid compared to just 16% with standard phrasing (girginzer, Comsmides)

con - nested sets hypothesis - facilitation occurs when relationship btwn sets and subsets is made transparent (Macchi)

other evidence shows differences in motivation and prior ability influence the extent to which ppl get the right answer with frequency formats……… (Brase)

ppl at top universities outperformed others…… ppl paid outperformed those who werent